Social media study of public opinions on potential COVID-19 vaccines: informing dissent, disparities, and dissemination

•We adopt a human-guided machine learning framework to capture public opinions on COVID-19 vaccines.•Public opinion varies across user characteristics.•The U.S. public is most concerned about the safety, effectiveness, and political issues.•Improving personal pandemic experience increases the vaccine acceptance level. Background The current development of vaccines for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is unprecedented. Little is known, however, about the nuanced public opinions on the vaccines on social media. Methods We adopted a human-guided machine learning framework using more than six million tweets from almost two million unique Twitter users to capture public opinions on the vaccines for SARS-CoV-2, classifying them into three groups: pro-vaccine, vaccine-hesitant, and anti-vaccine. After feature inference and opinion mining, 10,945 unique Twitter users were included in the study population. Multinomial logistic regression and counterfactual analysis were conducted. Results Socioeconomically disadvantaged groups were more likely to hold polarized opinions on coronavirus disease 2019 (COVID-19) vaccines, either pro-vaccine (B=0.40,SE=0.08,P